This rejoinder keeps the original focus on statistical computing pertaining to the correlation of student achievement between mathematics and science from the Trend in Mathematics and Science Study (TIMSS). Albeit the availability of student performance data in TIMSS and the emphasis of the inter-subject connection in the Next Generation Science Standards in the U.S., the major TIMSS reports did not consistently include the correlational findings of student achievements, nor did the TIMSS macro, JACKREGPV per Mirazchiyski’s recommendation, meet the need of correlation computing between each pair of plausible values (PV) in mathematics and science. In addition, an inflation of Type I error would be inevitable unless a feasible approach is taken to concurrently correlate the two sets of PVs from each subject. In this context, we adduced canonical correlation analysis (CCA) as a feasible approach that included considerations of both sampling weight and standard error adjustment due to complex sampling. More importantly, this rejoinder reveals two fundamental issues in the TIMSS literature: (1) Incompliance to the multiple imputation rule in reporting the TIMSS 1995 international findings (see Beaton et al., 1996a, b); (2) Need of substantially increasing the number of PV imputations as illustrated by the mainstream statistical computing in general (e.g., SAS Institute, 2015; STATA, 2017), and the recent international large scale assessments (ILSA) in particular.